



'''simple docstring'''






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import YolosImageProcessor

class 				a__(   unittest.TestCase      ):







    def __init__( self					:    str		,     __snake_case					:    int		,     __snake_case					:    List[Any]=7		,     __snake_case					:    Optional[int]=3		,     __snake_case					:    Dict=30		,     __snake_case					:    Dict=4_00		,     __snake_case					:    Tuple=True		,     __snake_case					:    Union[str, Any]=None		,     __snake_case					:    int=True		,     __snake_case					:    Optional[Any]=[0.5, 0.5, 0.5]		,     __snake_case					:    Any=[0.5, 0.5, 0.5]		,     __snake_case					:    Optional[int]=True		,     __snake_case					:    int=1 / 2_55		,     __snake_case					:    Any=True		,     ):
      # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
      a		:   Any	  =	size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
      a		:   Any	  =	parent
      a		:   Union[str, Any]	  =	batch_size
      a		:   str	  =	num_channels
      a		:   List[str]	  =	min_resolution
      a		:   Dict	  =	max_resolution
      a		:   str	  =	do_resize
      a		:   List[Any]	  =	size
      a		:   Optional[int]	  =	do_normalize
      a		:   str	  =	image_mean
      a		:   List[Any]	  =	image_std
      a		:   Optional[Any]	  =	do_rescale
      a		:   Optional[int]	  =	rescale_factor
      a		:   Any	  =	do_pad







    def      lowercase_			( self					:    Optional[Any]      ):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
      }







    def      lowercase_			( self					:    Dict		,     __snake_case					:    Union[str, Any]		,     __snake_case					:    Tuple=False      ):
      if not batched:
        a		:   Union[str, Any]	  =	image_inputs[0]
        if isinstance(__snake_case		,     Image.Image      ):
          a  ,      a		:   Tuple	  =	image.size
        else:
          a  ,      a		:   int	  =	image.shape[1], image.shape[2]
        if w < h:
          a		:   Dict	  =	int(self.size['shortest_edge'] * h / w      )
          a		:   Optional[int]	  =	self.size['shortest_edge']
        elif w > h:
          a		:   str	  =	self.size['shortest_edge']
          a		:   Dict	  =	int(self.size['shortest_edge'] * w / h      )
        else:
          a		:   int	  =	self.size['shortest_edge']
          a		:   Optional[Any]	  =	self.size['shortest_edge']

      else:
        a		:   List[str]	  =	[]
        for image in image_inputs:
          a  ,      a		:   Tuple	  =	self.get_expected_values([image]      )
          expected_values.append((expected_height, expected_width)      )
        a		:   Tuple	  =	max(__snake_case		,     key=lambda __snake_case      : item[0]      )[0]
        a		:   Any	  =	max(__snake_case		,     key=lambda __snake_case      : item[1]      )[1]

      return expected_height, expected_width

@require_torch
@require_vision
class 				a__(   lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       YolosImageProcessor if is_vision_available() else None







    def      lowercase_			( self					:    Dict      ):
      a		:   int	  =	YolosImageProcessingTester(self      )







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      return self.image_processor_tester.prepare_image_processor_dict()







    def      lowercase_			( self					:    str      ):
      a		:   Tuple	  =	self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(__snake_case		,     'image_mean'      )      )
      self.assertTrue(hasattr(__snake_case		,     'image_std'      )      )
      self.assertTrue(hasattr(__snake_case		,     'do_normalize'      )      )
      self.assertTrue(hasattr(__snake_case		,     'do_resize'      )      )
      self.assertTrue(hasattr(__snake_case		,     'size'      )      )







    def      lowercase_			( self					:    Dict      ):
      a		:   Optional[Any]	  =	self.image_processing_class.from_dict(self.image_processor_dict      )
      self.assertEqual(image_processor.size		,     {'shortest_edge': 18, 'longest_edge': 13_33}      )
      self.assertEqual(image_processor.do_pad		,     __snake_case      )

      a		:   Dict	  =	self.image_processing_class.from_dict(
          self.image_processor_dict		,     size=42		,     max_size=84		,     pad_and_return_pixel_mask=__snake_case      )
      self.assertEqual(image_processor.size		,     {'shortest_edge': 42, 'longest_edge': 84}      )
      self.assertEqual(image_processor.do_pad		,     __snake_case      )







    def      lowercase_			( self					:    Union[str, Any]      ):
      pass







    def      lowercase_			( self					:    str      ):
      # Initialize image_processing
      a		:   List[Any]	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      a		:   Optional[int]	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     Image.Image      )

      # Test not batched input
      a		:   Dict	  =	image_processing(image_inputs[0]		,     return_tensors='pt'      ).pixel_values

      a  ,      a		:   Any	  =	self.image_processor_tester.get_expected_values(__snake_case      )

      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      a  ,      a		:   Dict	  =	self.image_processor_tester.get_expected_values(__snake_case		,     batched=__snake_case      )

      a		:   Optional[int]	  =	image_processing(__snake_case		,     return_tensors='pt'      ).pixel_values
      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )







    def      lowercase_			( self					:    Dict      ):
      # Initialize image_processing
      a		:   List[str]	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random numpy tensors
      a		:   Union[str, Any]	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case		,     numpify=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     np.ndarray      )

      # Test not batched input
      a		:   Any	  =	image_processing(image_inputs[0]		,     return_tensors='pt'      ).pixel_values

      a  ,      a		:   Any	  =	self.image_processor_tester.get_expected_values(__snake_case      )

      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      a		:   Tuple	  =	image_processing(__snake_case		,     return_tensors='pt'      ).pixel_values

      a  ,      a		:   Optional[Any]	  =	self.image_processor_tester.get_expected_values(__snake_case		,     batched=__snake_case      )

      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )







    def      lowercase_			( self					:    Optional[Any]      ):
      # Initialize image_processing
      a		:   Tuple	  =	self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      a		:   Optional[Any]	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case		,     torchify=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     torch.Tensor      )

      # Test not batched input
      a		:   str	  =	image_processing(image_inputs[0]		,     return_tensors='pt'      ).pixel_values

      a  ,      a		:   List[str]	  =	self.image_processor_tester.get_expected_values(__snake_case      )

      self.assertEqual(
          encoded_images.shape		,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)		,     )

      # Test batched
      a		:   List[Any]	  =	image_processing(__snake_case		,     return_tensors='pt'      ).pixel_values

      a  ,      a		:   Optional[int]	  =	self.image_processor_tester.get_expected_values(__snake_case		,     batched=__snake_case      )

      self.assertEqual(
          encoded_images.shape		,     (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )		,     )







    def      lowercase_			( self					:    List[Any]      ):
      # Initialize image_processings
      a		:   List[Any]	  =	self.image_processing_class(**self.image_processor_dict      )
      a		:   Dict	  =	self.image_processing_class(do_resize=__snake_case		,     do_normalize=__snake_case		,     do_rescale=__snake_case      )
      # create random PyTorch tensors
      a		:   Dict	  =	prepare_image_inputs(self.image_processor_tester		,     equal_resolution=__snake_case		,     torchify=__snake_case      )
      for image in image_inputs:
        self.assertIsInstance(__snake_case		,     torch.Tensor      )

      # Test whether the method "pad" and calling the image processor return the same tensors
      a		:   List[str]	  =	image_processing_a.pad(__snake_case		,     return_tensors='pt'      )
      a		:   Dict	  =	image_processing_a(__snake_case		,     return_tensors='pt'      )

      self.assertTrue(
          torch.allclose(encoded_images_with_method['pixel_values']		,     encoded_images['pixel_values']		,     atol=1e-4      )      )







    @slow
    def      lowercase_			( self					:    Optional[Any]      ):
      # prepare image and target
      a		:   int	  =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
      with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'		,     'r'      ) as f:
        a		:   Dict	  =	json.loads(f.read()      )

      a		:   str	  =	{'image_id': 3_97_69, 'annotations': target}

      # encode them
      a		:   Union[str, Any]	  =	YolosImageProcessor.from_pretrained('hustvl/yolos-small'      )
      a		:   Optional[Any]	  =	image_processing(images=__snake_case		,     annotations=__snake_case		,     return_tensors='pt'      )

      # verify pixel values
      a		:   str	  =	torch.Size([1, 3, 8_00, 10_66]      )
      self.assertEqual(encoding['pixel_values'].shape		,     __snake_case      )

      a		:   Union[str, Any]	  =	torch.tensor([0.2796, 0.3138, 0.3481]      )
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,     __snake_case		,     atol=1e-4      )      )

      # verify area
      a		:   Optional[Any]	  =	torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,     __snake_case      )      )
      # verify boxes
      a		:   Any	  =	torch.Size([6, 4]      )
      self.assertEqual(encoding['labels'][0]['boxes'].shape		,     __snake_case      )
      a		:   int	  =	torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,     __snake_case		,     atol=1e-3      )      )
      # verify image_id
      a		:   Dict	  =	torch.tensor([3_97_69]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,     __snake_case      )      )
      # verify is_crowd
      a		:   str	  =	torch.tensor([0, 0, 0, 0, 0, 0]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,     __snake_case      )      )
      # verify class_labels
      a		:   Optional[Any]	  =	torch.tensor([75, 75, 63, 65, 17, 17]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,     __snake_case      )      )
      # verify orig_size
      a		:   List[str]	  =	torch.tensor([4_80, 6_40]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,     __snake_case      )      )
      # verify size
      a		:   int	  =	torch.tensor([8_00, 10_66]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,     __snake_case      )      )







    @slow
    def      lowercase_			( self					:    Dict      ):
      # prepare image, target and masks_path
      a		:   Optional[int]	  =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
      with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'		,     'r'      ) as f:
        a		:   Dict	  =	json.loads(f.read()      )

      a		:   Union[str, Any]	  =	{'file_name': '000000039769.png', 'image_id': 3_97_69, 'segments_info': target}

      a		:   List[Any]	  =	pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'      )

      # encode them
      a		:   Union[str, Any]	  =	YolosImageProcessor(format='coco_panoptic'      )
      a		:   Any	  =	image_processing(images=__snake_case		,     annotations=__snake_case		,     masks_path=__snake_case		,     return_tensors='pt'      )

      # verify pixel values
      a		:   Union[str, Any]	  =	torch.Size([1, 3, 8_00, 10_66]      )
      self.assertEqual(encoding['pixel_values'].shape		,     __snake_case      )

      a		:   List[str]	  =	torch.tensor([0.2796, 0.3138, 0.3481]      )
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,     __snake_case		,     atol=1e-4      )      )

      # verify area
      a		:   Optional[Any]	  =	torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,     __snake_case      )      )
      # verify boxes
      a		:   Optional[Any]	  =	torch.Size([6, 4]      )
      self.assertEqual(encoding['labels'][0]['boxes'].shape		,     __snake_case      )
      a		:   int	  =	torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,     __snake_case		,     atol=1e-3      )      )
      # verify image_id
      a		:   List[str]	  =	torch.tensor([3_97_69]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,     __snake_case      )      )
      # verify is_crowd
      a		:   str	  =	torch.tensor([0, 0, 0, 0, 0, 0]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,     __snake_case      )      )
      # verify class_labels
      a		:   List[Any]	  =	torch.tensor([17, 17, 63, 75, 75, 93]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,     __snake_case      )      )
      # verify masks
      a		:   Optional[Any]	  =	82_28_73
      self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,     __snake_case      )
      # verify orig_size
      a		:   Union[str, Any]	  =	torch.tensor([4_80, 6_40]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,     __snake_case      )      )
      # verify size
      a		:   Union[str, Any]	  =	torch.tensor([8_00, 10_66]      )
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,     __snake_case      )      )




'''simple docstring'''






from __future__ import annotations

lowerCAmelCase:   str					=		'Muhammad Umer Farooq'
lowerCAmelCase:   List[str]					=		'MIT'
lowerCAmelCase:   Tuple					=		'1.0.0'
lowerCAmelCase:   List[Any]					=		'Muhammad Umer Farooq'
lowerCAmelCase:   Optional[Any]					=		'contact@muhammadumerfarooq.me'
lowerCAmelCase:   Dict					=		'Alpha'

import re
from html.parser import HTMLParser
from urllib import parse

import requests

class 				a__(   lowerCamelCase__      ):







    def __init__( self					:    Dict		,     __snake_case					:    str      ):
      super().__init__()
      a		:   list[str]	  =	[]
      a		:   List[Any]	  =	domain







    def      lowercase_			( self					:    Dict		,     __snake_case					:    str		,     __snake_case					:    list[tuple[str, str | None]]      ):
      # Only parse the 'anchor' tag.
      if tag == "a":
        # Check the list of defined attributes.
        for name, value in attrs:
          # If href is defined, and not empty nor # print it.
          if name == "href" and value != "#" and value != "":
            # If not already in urls.
            if value not in self.urls:
              a		:   Tuple	  =	parse.urljoin(self.domain		,     __snake_case      )
              self.urls.append(__snake_case      )
def  lowerCamelCase__			(		_A							):
  return ".".join(get_sub_domain_name(_A							).split('.'							)[-2:]							)
def  lowerCamelCase__			(		_A							):
  return parse.urlparse(_A							).netloc
def  lowerCamelCase__			(		_A = "https://github.com"							):
  a		:   Any	  =	get_domain_name(_A							)

  # Initialize the parser
  a		:   Tuple	  =	Parser(_A							)

  try:
    # Open URL
    a		:   List[Any]	  =	requests.get(_A							)

    # pass the raw HTML to the parser to get links
    parser.feed(r.text							)

    # Get links and loop through
    a		:   Union[str, Any]	  =	set()
    for link in parser.urls:
      # open URL.
      # read = requests.get(link)
      try:
        a		:   int	  =	requests.get(_A							)
        # Get the valid email.
        a		:   Optional[Any]	  =	re.findall('[a-zA-Z0-9]+@' + domain					,     read.text							)
        # If not in list then append it.
        for email in emails:
          valid_emails.add(_A							)
      except ValueError:
        pass
  except ValueError:
    raise SystemExit(1							)

  # Finally return a sorted list of email addresses with no duplicates.
  return sorted(_A							)


if __name__ == "__main__":
     lowerCAmelCase:   Any					=		emails_from_url('https://github.com')
     print(F"{len(emails)} emails found:")
     print('\n'.join(sorted(emails)))